Early works focus on utilizing hand-crafted features or modifying the structure of existing
neural networks [1]}, [2]}, [3]}, [4]}, [5]} to detect face forgery.
Yang \(\)  [1]} utilize the inconsistency of the head pose estimated from the central face and the whole face to identify manipulated faces.
MesoNet [4]} designs a shallow neural network that consists of two inception modules and two classic convolution layers.
Though sound performances were achieved at that time, those methods are incapable of reliably detecting face forgery now due to the rapid development of face forgery technology.
Especially when powerful general feature extractors like xception [8]} are applied to forgery detection, the performance of early works is even more unsatisfactory.
Therefore, the research mainstream is gradually turning to approaches which introduce different information and prior knowledge into the backbone network to detect face forgery
 [9]}, [10]}, [11]}.
Dang \(\)  [9]} introduce location information of manipulated regions to guide the network to focus on key regions.
Qi \(\)  [10]} exploit bioinformatics that skin color will present minuscule changes periodically due to blood pumping through the face.
Face X-ray [14]} innovatively uses self-generated data to train the network to locate blending boundaries, which greatly improves the generalization ability.
Two-branch [11]} utilizes fixed filter banks to extract frequency information, which limits the ability to extract discriminative features.
In our work, we exploit a simple and effective module to adaptively mine frequency clues.
